Liu Liangchen, Liu Jianfei, Santra Bikash, Parnell Christopher, Mukherjee Pritam, Mathai Tejas, Zhu Yingying, Anand Akshaya, Summers Ronald M
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of Health, United States of America.
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of Health, United States of America.
Comput Med Imaging Graph. 2025 Jan;119:102458. doi: 10.1016/j.compmedimag.2024.102458. Epub 2024 Nov 28.
Multiple intravenous contrast phases of CT scans are commonly used in clinical practice to facilitate disease diagnosis. However, contrast phase information is commonly missing or incorrect due to discrepancies in CT series descriptions and imaging practices. This work aims to develop a classification algorithm to automatically determine the contrast phase of a CT scan. We hypothesize that image intensities of key organs (e.g. aorta, inferior vena cava) affected by contrast enhancement are inherent feature information to decide the contrast phase. These organs are segmented by TotalSegmentator followed by generating intensity features on each segmented organ region. Two internal and one external dataset were collected to validate the classification accuracy. In comparison with the baseline ResNet classification method that did not make use of key organs features, the proposed method achieved the comparable accuracy of 92.5% and F1 score of 92.5% in one internal dataset. The accuracy was improved from 63.9% to 79.8% and F1 score from 43.9% to 65.0% using the proposed method on the other internal dataset. The accuracy improved from 63.5% to 85.1% and the F1 score from 56.4% to 83.9% on the external dataset. Image intensity features from key organs are critical for improving the classification accuracy of contrast phases of CT scans. The classification method based on these features is robust to different scanners and imaging protocols from different institutes. Our results suggested improved classification accuracy over existing approaches, which advances the application of automatic contrast phase classification toward real clinical practice. The code for this work can be found here: (https://github.com/rsummers11/CT_Contrast_Phase_Classifier).
CT扫描的多个静脉造影期在临床实践中常用于辅助疾病诊断。然而，由于CT序列描述和成像操作存在差异，造影期信息常常缺失或错误。这项工作旨在开发一种分类算法，以自动确定CT扫描的造影期。我们假设，受造影剂增强影响的关键器官（如主动脉、下腔静脉）的图像强度是决定造影期的固有特征信息。通过TotalSegmentator对这些器官进行分割，然后在每个分割的器官区域生成强度特征。收集了两个内部数据集和一个外部数据集来验证分类准确性。与未使用关键器官特征的基线ResNet分类方法相比，所提出的方法在一个内部数据集中达到了92.5%的可比准确率和92.5%的F1分数。在另一个内部数据集上使用所提出的方法，准确率从63.9%提高到79.8%，F1分数从43.9%提高到65.0%。在外部数据集上，准确率从63.5%提高到85.1%，F1分数从56.4%提高到83.9%。关键器官的图像强度特征对于提高CT扫描造影期的分类准确性至关重要。基于这些特征的分类方法对于来自不同机构的不同扫描仪和成像协议具有鲁棒性。我们的结果表明，与现有方法相比，分类准确性有所提高，这推动了自动造影期分类在实际临床实践中的应用。这项工作的代码可在此处找到：(https://github.com/rsummers11/CT_Contrast_Phase_Classifier)。
